MPRA Munich Personal RePEc Archive

Estimates of the steady state growth rates for the Scandinavian countries: a knowledge economy approach

Paolo Casadio and Antonio Paradiso and B. Bhaskara Rao

30 May 2011

Online at https://mpra.ub.uni-muenchen.de/31606/ MPRA Paper No. 31606, posted 16 June 2011 13:06 UTC Estimates of the Steady State Growth Rates

for the Scandinavian Countries: A Knowledge Economy Approach

Paolo Casadio [email protected] Intesa Sanpaolo Bank Group, Risk Management, Rome ()

Antonio Paradiso [email protected] Department of Economics, University of Rome La Sapienza, Rome (Italy)

B. Bhaskara Rao [email protected] School of Economics and Finance, University of Western Sydney, Sydney ()

Abstract

This paper estimates the steady state growth rate for Scandinavian countries with a “knowledge economy” approach. We shall use an extended version of the Solow (1956) growth model, in which total factor productivity is assumed to be a function of human capital (measured by average years of education), trade openness and investment ratio. Using this framework we show that these factors, and in particular the education variable, have played an important role to determine the long run growth rates of the Scandinavian countries. Some policy measures are identified to improve the long-run growth rates for these countries.

Keywords: Endogenous growth models, Trade openness, human capital, investment ratio, Steady state growth rate, Scandinavian countries

JEL Classification: C22, O52, O40

1. Introduction

During the second half of the 1990s the Scandinavian countries (, , and ) were one of the most successful economies in the OECD countries. These Scandinavian countries had relatively high average GDP growth from 1995 to 2010 (2.5% in Norway, 2.6% in Sweden, and 2.9% in Finland) in comparison to the average growth rate of 1.8% in the 15 EU countries. These high growth rates seem to have been caused by the openness, education, and investment ratio and these variables are some of the key drivers of competitiveness and growth in a knowledge based economy. All the three Scandinavian countries are near the top of the Knowledge Economic Index (KEI) of the World Bank and suggest that these three variables have played an important role in explaining the long-term growth rates of Sweden, Finland and Norway. We investigate this aspect with an extended version of the Solow (1956) growth model by incorporating education, trade openness, and investment ratio as key determinants of the long-run growth rate. Our approach broadly follows the specification and methodology in Rao (2010) and Paradiso and Rao (2011).

The paper is organized as follows. In Section 2 we illustrate the characteristics of Scandinavian model in the light of the knowledge economy framework. Section 3 presents the specification of the model and the implications for the estimates of the long run growth rate, which is the same as steady state growth rate (SSGR) in the Solow growth model. Section 4 presents our empirical results and Section 5 concludes.

2. Scandinavian Countries as Knowledge Economies

In the past few decades where countries have experienced the effects of globalization and technical innovations, knowledge has become the key driver of competitiveness and economic growth. Dahlman and Anderson (2000) define knowledge economy as “one that encourages its organization and people to acquire, create, disseminate and use (codified and tacit) knowledge more effectively for greater economic and social development”. Dereck et al. (2004) postulated that knowledge economy is based on four pillars: educated and skilled workers; effective innovation system of firms, research centers, universities, and other organizations; modern and adequate information of infrastructure to facilitate the information dissemination; economic and institutional regimes to provide incentives for the efficient use of knowledge. In essence, these authors postulated that the amount of knowledge and how it is used are key determinants of total factor productivity (TFP). Strengthening the above four pillars will lead to an increase in the pool of knowledge available for economic production.

The three Scandinavian countries can be defined as knowledge economies according to these characteristics. Basing on the work of Dereck et al. (2004), World Bank developed an index called Knowledge Economy Index (KEI). The KEI is an economic indicator to measure a country’s ability to generate, adopt and diffuse knowledge. KEI summarizes each country’s performance on 12 variables corresponding to the four knowledge economy pillars introduced above. Variables are normalized on a scale of 0 (worst) and 10 (best). In Figure 1 we make an over-time comparison of KEI of some countries in terms of their relative performance for two points in time viz., 1995 and 2009. Countries above the diagonal line have made an improvement in the KEI in 2009 compared to 1995, whereas countries below the line experienced a decline. As we can see, Finland, Sweden, and Norway show very high value of KEI although Finland’s KEI in 2009 is a bit smaller than in 1995. Table 1 presents the most recent KEI (2009) and its four components for the best 5 countries, out of a total of 146 countries. Finland, Sweden, and Norway are between , which tops the list, and Norway at the end of the list.

The empirical indicators used for the estimations for the four components are the following. Economic and institutional regime: To proxy for the innovation system, we used trade openness index as indicator of the level of economic and institutional regime operating in the country. An open country is a country with (a) low tariff and non-tariff barriers on trade, (b) low barriers to technology transfers and (c) low power of national monopolies in the area such as telecommunications, air transport, and the finance and insurances industries (Houghton and Sheehan (2000)). Innovation system: Trade openness is perceived by many authors to have a positive impact on efficiency and innovation in the economy1. The idea is that the international trade leads to faster diffusion of technology, and hence higher productivity growth. In addition there are also spillover effects due to “learning by doing” gains and better management practices triggered by the new technology leading the firms to the best practice technology (Krugman (1987)). Human capital and education: One commonly used measure of human capital is the average years of schooling of the adult population. Average years of schooling is clearly a stock measure and reflects the accumulated educational investment embodied in the current labour force. Information infrastructure: Empirical assessments of the effects of ICTs on aggregate output and economic growth typically entail the use of ICT investment. However, due to the non availability of this series for a long time span and the importance also of non-ICT investments in economic growth, we use the aggregate series of investment (as a ratio of GDP) in our estimations.

1 See for example Jenkins (1995), Baldwin and Gu (2004), and Greenway and Kneller (2004). Figure 1

Knowledge Economic Index by Countries: 1995 and the most Recent Year (2009)

10 M o NL SE s 9.5 UK DN t FI CA NO

9 AU US r DE e WE G7 c 8.5 e SG JP n t 8 8 8.5 9 9.5 10

1995

Source: World Bank-Knowledge Assessment Methodology (KAM), www.worldbank.org/kam. Notes: Countries above the diagonal line have made an improvement in the KEI compared to 1995, whereas countries below the line experienced a regression. Legend: DN = Denmark; SE = Sweden; FI = Finland; NL = Netherland; US = U.S.A.; No = Norway; UK =